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Prexion: Probabilistic Matching-Based compression to Mitigate compression Side Channel Attacks Against HTTPS

IEEE ACCESS 2025年 13卷 86069-86082页

作者： Kim, Hyoungshick Hyun, Sangwon Sungkyunkwan Univ Dept Comp Sci & Engn Suwon 16419 Gyeonggi Do South Korea Myongji Univ Div Comp Informat & Commun Engn Yongin 17058 Gyeonggi Do South Korea

Web servers typically use data compression and encryption to optimize data transmission and protect user privacy. However, compression can inadvertently introduce vulnerabilities to compression side channel attacks (CSCA), which exploit the relationship between data redundancy and compression size to infer sensitive information. In this paper, we introduce Prexion, a novel compression system designed to counter CSCA while maintaining high compression efficiency. Prexion leverages a probabilistic mechanism to selectively include or exclude overlapping sequences in web page data during compression, making it difficult for attackers to exploit patterns in compressed sizes. This approach preserves the benefits of compression without compromising security. Through extensive experiments with real-world web pages, Prexion demonstrates superior performance, achieving up to 4.9 times better compression ratios than the current state-of-the-art solution, Debreach, for web pages with significant amounts of sensitive data. Furthermore, Prexion ensures robust security, with attack success rates consistently below 5% under recommended configurations.

关键词： Web pages Web servers HTTP Probabilistic logic Resilience compression algorithms Side-channel attacks Electronic mail Redundancy Process control Data privacy HTTPS compression side channel attack

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Quality Scores compression of Genomic Sequencing Data: A Comprehensive Review and Performance Evaluation

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2025年第2期22卷 817-831页

作者： Liu, Yuansheng Tang, Tao Zhu, Zexuan Zeng, Xiangxiang Zou, Quan Li, Keqin Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China Nanjing Univ Posts & Telecommun Sch Modern Posts Nanjing 210049 Jiangsu Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu 610054 Peoples R China Natl Supercomp Ctr Changsha 410082 Hunan Peoples R China State Univ NY Dept Comp Sci New Paltz NY 12561 USA

Advanced sequencing technologies have profoundly revolutionized biology and produced vast amounts of raw sequencing data during the past decades. The enormous amount of sequencing data proposed significant challenges of data storage and transmission. Compressing a big file into a small file is an encouraging method to tackle these challenges. Howver, it has been found that traditional text data compression algorithms are not well-suited for handing the vast sequencing datasets. Therefore, several algorithms are designed specifically for the efficient compression of sequencing data. Recently, considerable research has been devoted to compressing quality scores stored in the FASTQ format file, resulting in substantial advances in compression performance. Despite these advances, there has been no systematic review and evaluation of these algorithms or software. In this review, we aim to conduct a broad review of the existing quality score compression algorithms. We mainly discuss those algorithms from two categories, i.e., lossless and lossy compression. Additionally, we benchmark the compression performance of 12 tools using 14 real datasets. We anticipate that our review will provide practical guidance for others seeking to design an appropriate algorithm for compressing quality scores.

关键词： Sequential analysis Genomics Bioinformatics compression algorithms Data compression Image coding Electronic mail Data mining Computational biology Training Lossless compression lossy compression quality scores compression sequencing data compression

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Joint-Way compression for LDPC Neural Decoding Algorithm With Tensor-Ring Decomposition

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IEEE ACCESS 2023年 11卷 22871-22879页

作者： Liang, Yuanhui Lam, Chan-Tong Ng, Benjamin K. Macao Polytech Univ Fac Appl Sci Macau 999078 Peoples R China

In this paper, we propose low complexity joint-way compression algorithms with Tensor-Ring (TR) decomposition and weight sharing to further lower the storage and computational complexity requirements for low density parity check (LDPC) neural decoding. Compared with Tensor-Train (TT) decomposition, TR decomposition is more flexible for the selection of ranks, and is also conducive to the use of rank optimization algorithms. In particular, we use TR decomposition to decompose not only the weight parameter matrix of Neural Normalized Min-Sum (NNMS)+ algorithm, but also the message matrix transmitted between variable nodes and check nodes. Furthermore, we combine the TR decomposition and weight sharing algorithm, called joint-way compression, to further lower the complexity of LDPC neural decoding algorithm. We show that the joint-way compression algorithm can achieve better compression efficiency than a single compression algorithm while maintaining a comparable bit error rate (BER) performance. From the numerical experiments, we found that all the compression algorithms with appropriate selection of ranks give almost no performance degradation and that the TRwm-ssNNMS+ algorithm, which combines the spatial sharing and TR decomposition of both weight and message matrix, has the best compression efficiency. Compared with our TT-NNMS+ algorithm proposed in Yuanhui et al. (2022), the number of parameters is reduced by about 70 times and the number of multiplications is reduced by about 6 times.

关键词： Decoding Artificial neural networks Iterative decoding Tensors Matrix decomposition compression algorithms Training Joint-way compression weight sharing tensor ring decomposition LDPC neural decoding

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LAMP: Improving compression Ratio for AMR Applications via Level Associated Mapping-Based Preconditioning

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IEEE TRANSACTIONS ON COMPUTERS 2023年第12期72卷 3370-3382页

作者： Li, Yida Luo, Huizhang Li, Fenfang Wang, Junqi Li, Kenli Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Beijing Inst Gen Artificial Intelligence BIGAI Beijing 100080 Peoples R China

Data compression can efficiently reduce the memory and persistence storage cost, which is highly desirable in modern computing systems, such as enterprise, cloud, and High-Performance Computing (HPC) environments. However, the main challenges of existing data compressors are the insufficient compression ratio and low throughput. This paper focuses on improving the compression ratio of state-of-the-art lossy compression algorithms from the view of applications. Besides, we also use the characteristics of the applications to reduce the runtime overhead. To this end, we explore the idea with Adaptive Mesh Refinement (AMR), which is widely adopted as a computational technique to reduce the amount of computation and memory required in scientific simulations. We propose Level Associated Mapping-based Preconditioning (LAMP) to improve the storage efficiency of AMR applications. The main idea is twofold. First, we utilize the high similarities among the adjacent AMR levels to precondition the data prior to compression. Second, AMR has a unique characteristic of grid structures. We utilize grid structures to rebuild a level associated mapping table, which significantly reduces the runtime overhead of LAMP. Thanks to the optimization techniques of General Matrix Multiplication (GEMM), we further accelerate the process of rebuilding AMR hierarchy for LAMP. Besides, we also block multiple adjacent coordinates within a box and further improve cache locality. The experimental results show that the compression ratios of LAMP are improved up to 63.8% compared to directly compressing the data.

关键词： Runtime Data compression compression algorithms Adaptive mesh refinement Predictive models Optimization Compressors high-performance computing (HPC) adaptive mesh refinement (AMR) data preconditioning AMR level associated mapping cache locality

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Area Efficient compression for Floating-Point Feature Maps in Convolutional Neural Network Accelerators

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第2期70卷 746-750页

作者： Yan, Bai-Kui Ruan, Shanq-Jang Natl Taiwan Univ Sci & Technol Dept Elect & Comp Engn Taipei 10607 Taiwan

Since Convolutional neural networks (CNNs) need massive computing resources, lots of computing architectures are proposed to improve the throughput and energy efficiency of the computing. However, those computing architectures need high data movement between the chip and off-chip memories, which causes high energy consumption on the off-chip memory;thus, the feature map (fmap) compression has been discussed for reducing the data movement. Therefore, the design of fmap compression becomes one of the main researches on CNN accelerator for energy efficiency on the off-chip memory. In this brief, we proposed floating-point (FP) fmap compression for a hardware accelerator which includes hardware design and a compression algorithm. This can apply quantization methods such as ternary neural quantization (TTQ), which only quantized weights with little or no degradation in accuracy and reduces the computation cost. In addition to the zero compression, we also compress nonzero values in the fmap based on the FP format. The compression algorithm achieves low area overhead and a similar compression ratio compared with the state-of-the-art on ILSVRC 2012 dataset.

关键词： Encoding Codes Convolutional neural networks compression algorithms Quantization (signal) Symbols Energy efficiency compression convolutional neural networks (CNNs) CNN accelerators floating-point (FP) area efficient

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Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy compression 24

Accelerating Communication in Deep Learning Recommendation M...

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2024 International Conference for High Performance Computing, Networking, Storage and Analysis

作者： Feng, Hao Zhang, Boyuan Ye, Fanjiang Si, Min Chu, Ching-Hsiang Tian, Jiannan Yin, Chunxing Deng, Stunner Hao, Yuchen Balaji, Pavan Geng, Tong Tao, Dingwen Indiana Univ Bloomington IN USA Meta Menlo Pk CA USA Univ Rochester Rochester NY USA Chinese Acad Sci Inst Comp Technol SKLP Beijing Peoples R China

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

DLRM is a state-of-the-art recommendation system model that has gained widespread adoption across various industry applications. The large size of DLRM models, however, necessitates the use of multiple devices/GPUs for efficient training. A significant bottleneck in this process is the time-consuming all-to-all communication required to collect embedding data from all devices. To mitigate this, we introduce a method that employs error-bounded lossy compression to reduce the communication data size and accelerate DLRM training. We develop a novel error-bounded lossy compression algorithm, informed by an in-depth analysis of embedding data features, to achieve high compression ratios. Moreover, we introduce a dual-level adaptive strategy for error-bound adjustment, spanning both table-wise and iteration-wise aspects, to balance the compression benefits with the potential impacts on accuracy. We further optimize our compressor for PyTorch tensors on GPUs, minimizing compression overhead. Evaluation shows that our method achieves a 1.38x training speedup with a minimal accuracy impact.

关键词： Training Deep learning Analytical models Accuracy Tensors High performance computing Computational modeling Industry applications Recommender systems compression algorithms

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On the Convergence of Federated Learning algorithms Without Data Similarity

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IEEE TRANSACTIONS ON BIG DATA 2025年第2期11卷 659-668页

作者： Beikmohammadi, Ali Khirirat, Sarit Magnusson, Sindri Stockholm Univ Dept Comp & Syst Sci S-16425 Stockholm Sweden King Abdullah Univ Sci & Technol KAUST Thuwal 23955 Saudi Arabia

Data similarity assumptions have traditionally been relied upon to understand the convergence behaviors of federated learning methods. Unfortunately, this approach often demands fine-tuning step sizes based on the level of data similarity. When data similarity is low, these small step sizes result in an unacceptably slow convergence speed for federated methods. In this paper, we present a novel and unified framework for analyzing the convergence of federated learning algorithms without the need for data similarity conditions. Our analysis centers on an inequality that captures the influence of step sizes on algorithmic convergence performance. By applying our theorems to well-known federated algorithms, we derive precise expressions for three widely used step size schedules: fixed, diminishing, and step-decay step sizes, which are independent of data similarity conditions. Finally, we conduct comprehensive evaluations of the performance of these federated learning algorithms, employing the proposed step size strategies to train deep neural network models on benchmark datasets under varying data similarity conditions. Our findings demonstrate significant improvements in convergence speed and overall performance, marking a substantial advancement in federated learning research.

关键词： compression algorithms Federated learning gradient methods machine learning compression algorithms federated learning gradient methods machine learning

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compression Algorithm for Extended Grid Coordinates used in Mobile Robots Navigation 24

Compression Algorithm for Extended Grid Coordinates used in ...

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International Conference on Automation, Quality and Testing, Robotics (AQTR)

作者： Marcu, Cosmin Tigauan, Claudia Tech Univ Cluj Napoca Automat Dept Cluj Napoca Romania

ISBN: (纸本)9798350361940;9798350361933;9798350361919

The data compression algorithms are used in different fields to manipulate and store big amount of information. The data exchange between different systems has become crucial nowadays and it actively contributes to the systems' efficiency. Besides the applications for file archiving, multimedia and communication, compression algorithms are used lately in embedded systems applications as well. In hardware environments with limited resources such as embedded systems, compression algorithms are used to save storage space and to improve the power consumption by reducing the amount of data that needs to be handled. This paper proposes a compression algorithm for GPS navigation data applied on extended grid coordinates. In addition, the paper presents a navigation method for mobile robots which uses compressed coordinates directly. The algorithm is validated by numerical identification of the results in different scenarios.

关键词： compression algorithms navigation mobile robots grid mapping extended grid coordinates

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Dynamic Rearrangement compression Algorithm for Intelligent Connected Vehicles

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第10期71卷 10350-10360页

作者： Wu, Yujing Li, Jinze Xu, Yinan Chung, Jin-Gyun Dai, Yilin Xu, Yihu Yanbian Univ Coll Engn Yanji 133002 Peoples R China Jeonbuk Natl Univ IT Convergence Res Ctr Div Elect Engn Jeonju 54896 South Korea

The intelligent vehicle terminal T-BOX of intelligent connected vehicles (ICVs) is directly connected to the vehicle interface and constantly exchanges real-time data inside and outside the vehicle, which significantly increases the communication pressure on the in-vehicle network. Excessive data will increase the load on the channel due to the limited bandwidth of the vehicle. Although the existing data compression algorithm can effectively reduce the communication pressure, it requires significant driving data to count the number of signal changes, which increases the preliminary labor cost. Meanwhile, it is difficult to meet the stable compression rate using a static configuration when a large amount of CAN data is transmitted with fluctuating data changes, which cannot satisfy the requirement of reducing the bus load rate, and satisfying the real-time requirements of ICV is challenging. Thus, we propose a real-time dynamic data compression algorithm for vehicles to satisfy such requirements. The experiments show that for the frequently changing signals, the proposed algorithm can improve the compression rate by about 10% compared with the existing compression algorithm. The execution time of the compression process is 4.55 mu s at a clock frequency of 400 MHz and the decompression process execution time is 86.48 mu s. Compared with the bus load when the original message is transmitted, the proposed algorithm can reduce the bus load by 19% on average. In the future, a stable compression rate will be crucial when loading authentication codes on the CAN to ensure its security.

关键词： Data compression Heuristic algorithms compression algorithms Real-time systems Engines Electronic mail Planning Automotive electronics data compression electronic control units (ECU) intelligent connected vehicle (ICV)

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Data-Aware Adaptive Pruning Model compression Algorithm Based on a Group Attention Mechanism and Reinforcement Learning (vol 10, pg 82396, 2022)

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IEEE ACCESS 2022年 10卷 134769-134769页

作者： Yang, Zhi Zhai, Yuan Xiang, Yi Wu, Jianquan Shi, Jinliang Wu, Ying Chongqing Univ Sci & Technol Sch Intelligent Technol & Engn Chongqing 401331 Peoples R China

The success of convolutional neural networks (CNNs) benefits from the stacking of convolutional layers, which improves the model’s receptive field for image data but also causes a decrease in inference speed. To improve the inference speed of large convolutional network models without sacrificing performance indicators too much, a data-aware adaptive pruning algorithm is proposed. The algorithm consists of two parts, namely, a channel pruning method based on the attention mechanism and a data-aware pruning policy based on reinforcement learning. Experimental results on the CIFAR-100 dataset show that the performance of the proposed pruning algorithm is reduced by only 2.05%, 1.93% and 5.66% after pruning the VGG19, ResNet56 and EfficientNet networks, respectively, but the speedup ratios are 3.63, 3.35, and 1.14, respectively, and the comprehensive pruning performance is the best. In addition, the generalization ability of the reconstruction model is evaluated on the ImageNet dataset and FGVC Aircraft dataset, and the performance of the proposed algorithm is the best, which shows that the proposed algorithm learns data-related information in the pruning process, that is, it is a data-aware algorithm.

关键词： Adaptation models Organizations Reinforcement learning compression algorithms

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